Paper Info
Title
Am-fm modulation features for music instrument signal analysis and recognition
Abstract
In this paper, we explore a nonlinear AM-FM model to extract alternative features for music instrument recognition tasks. Amplitude and frequency micro-modulations are measured in musical signals and are employed to model the existing information. The features used are the multiband mean instantaneous amplitude (mean-IAM) and mean instantaneous frequency (mean-IFM) modulation. The instantaneous features are estimated using the multiband Gabor Energy Separation Algorithm (Gabor-ESA). An alternative method, the iterativeESA is also explored; and initial experimentation shows that it could be used to estimate the harmonic content of a tone. The Gabor-ESA is evaluated against and in combination with Mel frequency cepstrum coefficients (MFCCs) using both static and dynamic classifiers. The method used in this paper has proven to be able to extract the fine-structured modulations of music signals; further, it has shown to be promising for recognition tasks accomplishing an error rate reduction up to 60% for the best recognition case combined with MFCCs.
Year
Venue
Keywords
2012
Signal Processing Conference
Gabor filters,amplitude modulation,feature extraction,frequency modulation,musical instruments,signal classification,source separation,AM-FM modulation feature,Gabor-ESA,MFCC,Mel frequency cepstrum coefficient,alternative feature extraction,amplitude micromodulation,dynamic classifier,error rate reduction,fine-structured modulation,frequency micromodulation,harmonic content estimation,instantaneous feature estimation,iterative ESA,iterative energy separation algorithm,mean instantaneous frequency modulation,multiband Gabor energy separation algorithm,multiband mean instantaneous amplitude modulation,music instrument recognition task,music instrument signal analysis,music instrument signal recognition,nonlinear AM-FM model,static classifier,AM-FM modulations,energy separation algorithm,music processing,timbre classification
Field
DocType
ISSN
Mel-frequency cepstrum,Pattern recognition,Computer science,Word error rate,Feature extraction,Speech recognition,Amplitude modulation,Artificial intelligence,Frequency modulation,Instantaneous phase,Source separation,Modulation (music)
Conference
2219-5491
ISBN
Citations 
PageRank 
978-1-4673-1068-0
2
0.38
References 
Authors
13
2
Name
Order
Citations
PageRank
Athanasia Zlatintsi182.57
Petros Maragos2322.89